DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_4:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [2]:
project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')
In [3]:
#selecting first 3000 records
project_data = project_data[:3000]
project_data.shape
Out[3]:
In [4]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
project_data.head(2)
Out[4]:
In [5]:
print("Number of data points in resource data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[5]:
1.2 Data Analysis¶
In [6]:
# PROVIDE CITATIONS TO YOUR CODE IF YOU TAKE IT FROM ANOTHER WEBSITE.
# https://matplotlib.org/gallery/pie_and_polar_charts/pie_and_donut_labels.html#sphx-glr-gallery-pie-and-polar-charts-pie-and-donut-labels-py
y_value_counts = project_data['project_is_approved'].value_counts()
#print(type(y_value_counts))
print("Number of projects thar are approved for funding ", y_value_counts[1], ", (", (y_value_counts[1]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
print("Number of projects thar are not approved for funding ", y_value_counts[0], ", (", (y_value_counts[0]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
recipe = ["Accepted", "Not Accepted"]
data = [y_value_counts[1], y_value_counts[0]]
wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle="-"),
bbox=bbox_props, zorder=0, va="center")
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
ax.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment, **kw)
ax.set_title("Nmber of projects that are Accepted and not accepted")
plt.show()
1.2.1 Univariate Analysis: School State¶
In [7]:
# Pandas dataframe groupby count, mean: https://stackoverflow.com/a/19385591/4084039
temp = pd.DataFrame(project_data.groupby("school_state")["project_is_approved"].apply(np.mean)).reset_index()
# if you have data which contain only 0 and 1, then the mean = percentage (think about it)
temp.columns = ['state_code', 'num_proposals']
print(temp.head(1))
'''# How to plot US state heatmap: https://datascience.stackexchange.com/a/9620
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
data = [ dict(
type='choropleth',
colorscale = scl,
autocolorscale = False,
locations = temp['state_code'],
z = temp['num_proposals'].astype(float),
locationmode = 'USA-states',
text = temp['state_code'],
marker = dict(line = dict (color = 'rgb(255,255,255)',width = 2)),
colorbar = dict(title = "% of pro")
) ]
layout = dict(
title = 'Project Proposals % of Acceptance Rate by US States',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)',
),
)
fig = go.Figure(data=data, layout=layout)
offline.iplot(fig, filename='us-map-heat-map')
'''
Out[7]:
In [8]:
# https://www.csi.cuny.edu/sites/default/files/pdf/administration/ops/2letterstabbrev.pdf
temp.sort_values(by=['num_proposals'], inplace=True)
print("States with lowest % approvals")
print(temp.head(5))
print('='*50)
print("States with highest % approvals")
print(temp.tail(5))
In [9]:
#stacked bar plots matplotlib: https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html
def stack_plot(data, xtick, col2='project_is_approved', col3='total'):
ind = np.arange(data.shape[0])
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, data[col3].values)
p2 = plt.bar(ind, data[col2].values)
plt.ylabel('Projects')
plt.title('Number of projects aproved vs rejected')
plt.xticks(ind, list(data[xtick].values))
plt.legend((p1[0], p2[0]), ('total', 'accepted'))
plt.show()
In [10]:
def univariate_barplots(data, col1, col2='project_is_approved', top=False):
# Count number of zeros in dataframe python: https://stackoverflow.com/a/51540521/4084039
temp = pd.DataFrame(project_data.groupby(col1)[col2].agg(lambda x: x.eq(1).sum())).reset_index()
# Pandas dataframe grouby count: https://stackoverflow.com/a/19385591/4084039
temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']
temp.sort_values(by=['total'],inplace=True, ascending=False)
if top:
temp = temp[0:top]
stack_plot(temp, xtick=col1, col2=col2, col3='total')
print(temp.head(5))
print("="*50)
print(temp.tail(5))
In [11]:
univariate_barplots(project_data, 'school_state', 'project_is_approved', False)
SUMMARY: Every state has greater than 80% success rate in approval
1.2.2 Univariate Analysis: teacher_prefix¶
In [12]:
univariate_barplots(project_data, 'teacher_prefix', 'project_is_approved' , top=False)
1.2.3 Univariate Analysis: project_grade_category¶
In [13]:
univariate_barplots(project_data, 'project_grade_category', 'project_is_approved', top=False)
1.2.4 Univariate Analysis: project_subject_categories¶
In [14]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
In [15]:
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
project_data.head(2)
Out[15]:
In [16]:
univariate_barplots(project_data, 'clean_categories', 'project_is_approved', top=20)
In [17]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
In [18]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved category wise')
plt.xticks(ind, list(sorted_cat_dict.keys()))
plt.show()
In [19]:
for i, j in sorted_cat_dict.items():
print("{:20} :{:10}".format(i,j))
1.2.5 Univariate Analysis: project_subject_subcategories¶
In [20]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
In [21]:
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
project_data.head(2)
Out[21]:
In [22]:
univariate_barplots(project_data, 'clean_subcategories', 'project_is_approved', top=50)
In [23]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
In [24]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_sub_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_sub_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved state wise')
plt.xticks(ind, list(sorted_sub_cat_dict.keys()))
plt.show()
In [25]:
for i, j in sorted_sub_cat_dict.items():
print("{:20}:{:0}".format(i,j))
1.2.6 Univariate Analysis: Text features (Title)¶
In [26]:
#How to calculate number of words in a string in DataFrame: https://stackoverflow.com/a/37483537/4084039
word_count = project_data['project_title'].str.split().apply(len).value_counts()
word_dict = dict(word_count)
word_dict = dict(sorted(word_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(word_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(word_dict.values()))
plt.ylabel('Number of projects')
plt.xlabel('Number of words in project title')
plt.title('Words for each title of the project')
plt.xticks(ind, list(word_dict.keys()))
plt.show()
In [27]:
approved_title_word_count = project_data[project_data['project_is_approved']==1]['project_title'].str.split().apply(len)
approved_title_word_count = approved_title_word_count.values
rejected_title_word_count = project_data[project_data['project_is_approved']==0]['project_title'].str.split().apply(len)
rejected_title_word_count = rejected_title_word_count.values
In [28]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_title_word_count, rejected_title_word_count])
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project title')
plt.grid()
plt.show()
In [29]:
plt.figure(figsize=(10,3))
sns.kdeplot(approved_title_word_count,label="Approved Projects", bw=0.6)
sns.kdeplot(rejected_title_word_count,label="Not Approved Projects", bw=0.6)
plt.legend()
plt.show()
1.2.7 Univariate Analysis: Text features (Project Essay's)¶
In [30]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [31]:
approved_word_count = project_data[project_data['project_is_approved']==1]['essay'].str.split().apply(len)
approved_word_count = approved_word_count.values
rejected_word_count = project_data[project_data['project_is_approved']==0]['essay'].str.split().apply(len)
rejected_word_count = rejected_word_count.values
In [32]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_word_count, rejected_word_count])
plt.title('Words for each essay of the project')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project essays')
plt.grid()
plt.show()
In [33]:
plt.figure(figsize=(10,3))
sns.distplot(approved_word_count, hist=False, label="Approved Projects")
sns.distplot(rejected_word_count, hist=False, label="Not Approved Projects")
plt.title('Words for each essay of the project')
plt.xlabel('Number of words in each eassay')
plt.legend()
plt.show()
1.2.8 Univariate Analysis: Cost per project¶
In [34]:
# we get the cost of the project using resource.csv file
resource_data.head(2)
Out[34]:
In [35]:
# https://stackoverflow.com/questions/22407798/how-to-reset-a-dataframes-indexes-for-all-groups-in-one-step
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
price_data.head(2)
Out[35]:
In [36]:
# join two dataframes in python:
project_data = pd.merge(project_data, price_data, on='id', how='left')
In [37]:
approved_price = project_data[project_data['project_is_approved']==1]['price'].values
rejected_price = project_data[project_data['project_is_approved']==0]['price'].values
In [38]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_price, rejected_price])
plt.title('Box Plots of Cost per approved and not approved Projects')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Price')
plt.grid()
plt.show()
In [39]:
plt.figure(figsize=(10,3))
sns.distplot(approved_price, hist=False, label="Approved Projects")
sns.distplot(rejected_price, hist=False, label="Not Approved Projects")
plt.title('Cost per approved and not approved Projects')
plt.xlabel('Cost of a project')
plt.legend()
plt.show()
In [40]:
# http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable
x = PrettyTable()
x.field_names = ["Percentile", "Approved Projects", "Not Approved Projects"]
for i in range(0,101,5):
x.add_row([i,np.round(np.percentile(approved_price,i), 3), np.round(np.percentile(rejected_price,i), 3)])
print(x)
1.2.9 Univariate Analysis: teacher_number_of_previously_posted_projects
Please do this on your own based on the data analysis that was done in the above cells
In [41]:
# Checking the number of unique values in 'teacher_number_of_previously_posted_projects' column
# https://cmdlinetips.com/2018/01/how-to-get-unique-values-from-a-column-in-pandas-data-frame/
print(len(project_data['teacher_number_of_previously_posted_projects'].sort_values().unique()))
#reusing the function 'univariate_barplots' created at the beginning of the document
a=univariate_barplots(project_data, 'teacher_number_of_previously_posted_projects', 'project_is_approved', 30)
Observations:¶
- There is no relation between number of projects posted by the teachers and their chances of approval.
- The above stats say that there are teachers who have been rejected even thought they have the highest number of submissions and viceversa
1.2.10 Univariate Analysis: project_resource_summary
Please do this on your own based on the data analysis that was done in the above cells
Check if the presence of the numerical digits in the project_resource_summary effects the acceptance of the project or not. If you observe that presence of the numerical digits is helpful in the classification, please include it for further process or you can ignore it.
In [42]:
import re #importing regular expressions
#storing resource sumamries to a list
summaries = list(project_data['project_resource_summary'].values)
alphanum_flg = []
for i in summaries:
if re.search(r'[0-9]',i): #Checking if the summary contains numbers
alphanum_flg.append(int(1)) #assigning 1 for summaries containing numbers
else:
alphanum_flg.append(int(0)) #assigning 0 for summaries without numbers
project_data['alphanum_flg'] = alphanum_flg
#reusing the function 'univariate_barplots' created at the beginning of the document
univariate_barplots(project_data, 'alphanum_flg', 'project_is_approved', False)
Observations:¶
The percentage of acceptance is 84 and 88 for summaries without numbers and with numbers respectively. Therefore there is slightly higher chance of approval, if there are numbers in the summaries.
1.3 Text preprocessing¶
1.3.1 Essay Text¶
In [43]:
project_data.head(2)
Out[43]:
In [44]:
# printing some random essays.
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
In [45]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [46]:
sent = decontracted(project_data['essay'].values[2000])
print(sent)
print("="*50)
In [47]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [48]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [49]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
In [50]:
# Combining all the above statemennts
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_essays.append(sent.lower().strip())
In [51]:
# after preprocesing
preprocessed_essays[2000]
Out[51]:
1.3.2 Project title Text
In [52]:
project_data.head(2)
Out[52]:
In [53]:
#Printing a few random review summaries
for i in range(1,3000,1000):
sent = project_data['project_title'].values[i]
print(sent,'--- Row No:',i)
print("="*50)
In [54]:
# The above random records show that there are no URLs or HTML tags, but we will remove incase if there are any
from tqdm import tqdm #for status bar
from bs4 import BeautifulSoup #for html tags
preprocessed_title=[]
for title in tqdm(project_data['project_title'].values):
# To remove urls - https://stackoverflow.com/a/40823105/4084039
title = re.sub(r"http\S+", "", title)
# To remove all HTML tags
#https://stackoverflow.com/questions/16206380/python-beautifulsoup-how-to-remove-all-tags-from-an-element
title = BeautifulSoup(title, 'lxml').get_text()
# To split contractions - refer decontracted function defined above
title = decontracted(title)
# To remove alphanumerics (words with numbers in them) - https://stackoverflow.com/a/18082370/4084039
title = re.sub("\S*\d\S*", "", title).strip()
# To remove special characters - https://stackoverflow.com/a/5843547/4084039
title = re.sub('[^A-Za-z]+', ' ', title)
# To remove stop words from the summaries and convert to lowercase
title = ' '.join(e.lower() for e in title.split() if e.lower() not in stopwords)
preprocessed_title.append(title.strip())
1. 4 Preparing data for models¶
In [55]:
project_data.columns
Out[55]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data
- quantity : numerical
- teacher_number_of_previously_posted_projects : numerical
- price : numerical1.4.1 Vectorizing Categorical data¶
In [56]:
# we use count vectorizer to convert the values into one hot encoded features
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_categories'].values)
print(vectorizer.get_feature_names())
categories_one_hot = vectorizer.transform(project_data['clean_categories'].values)
print("Shape of matrix after one hot encodig ",categories_one_hot.shape)
In [57]:
# we use count vectorizer to convert the values into one hot encoded features
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_subcategories'].values)
print(vectorizer.get_feature_names())
sub_categories_one_hot = vectorizer.transform(project_data['clean_subcategories'].values)
print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)
Please do the similar feature encoding with state, teacher_prefix and project_grade_category also
In [58]:
# we use count vectorizer to convert the values into one hot encoded features
#https://cmdlinetips.com/2018/01/how-to-get-unique-values-from-a-column-in-pandas-data-frame/
#To get unique values from school_state column
school_state_lst=project_data['school_state'].unique()
vectorizer = CountVectorizer(vocabulary = school_state_lst, lowercase=False, binary=True)
vectorizer.fit(project_data['school_state'].values)
print(vectorizer.get_feature_names())
school_state_one_hot = vectorizer.transform(project_data['school_state'].values)
print("Shape of matrix after one hot encoding ",school_state_one_hot.shape)
In [59]:
# we use count vectorizer to convert the values into one hot encoded features
#https://cmdlinetips.com/2018/01/how-to-get-unique-values-from-a-column-in-pandas-data-frame/
#https://stackoverflow.com/questions/48090658/sklearn-how-to-incorporate-missing-data-when-one-hot-encoding
#replacing Nan values with 'Unknown'
project_data['teacher_prefix']=project_data['teacher_prefix'].replace(np.nan,'Unknown')
#fetching unique values
teacher_prefix_lst=project_data['teacher_prefix'].unique()
vectorizer = CountVectorizer(vocabulary = teacher_prefix_lst, lowercase=False, binary=True)
vectorizer.fit(project_data['teacher_prefix'].values)
print(vectorizer.get_feature_names())
teacher_prefix_one_hot = vectorizer.transform(project_data['teacher_prefix'].values)
print("Shape of matrix after one hot encoding ",teacher_prefix_one_hot.shape)
In [60]:
# we use count vectorizer to convert the values into one hot encoded features
#https://cmdlinetips.com/2018/01/how-to-get-unique-values-from-a-column-in-pandas-data-frame/
#To get unique values from project_grade_category column
grade_cat_lst=project_data['project_grade_category'].unique()
vectorizer = CountVectorizer(vocabulary = grade_cat_lst, lowercase=False, binary=True)
vectorizer.fit(project_data['project_grade_category'].values)
print(vectorizer.get_feature_names())
grade_cat_one_hot = vectorizer.transform(project_data['project_grade_category'].values)
print("Shape of matrix after one hot encoding ",grade_cat_one_hot.shape)
1.4.2 Vectorizing Text data¶
1.4.2.1 Bag of words¶
In [61]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_bow.shape)
1.4.2.2 Bag of Words on `project_title`
In [62]:
# you can vectorize the title also
# before you vectorize the title make sure you preprocess it
vectorizer = CountVectorizer(min_df=10)
title_bow = vectorizer.fit_transform(preprocessed_title)
print("Shape of matrix after one hot encodig ",title_bow.shape)
1.4.2.3 TFIDF vectorizer¶
In [63]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
text_tfidf = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_tfidf.shape)
1.4.2.4 TFIDF Vectorizer on `project_title`
In [64]:
# Similarly you can vectorize for title also
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
title_tfidf = vectorizer.fit_transform(preprocessed_title)
print("Shape of matrix after one hot encodig ",title_tfidf.shape)
1.4.2.5 Using Pretrained Models: Avg W2V¶
In [65]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
f = open(gloveFile,'r', encoding="utf8")
model = {}
for line in tqdm(f):
splitLine = line.split()
word = splitLine[0]
embedding = np.array([float(val) for val in splitLine[1:]])
model[word] = embedding
print ("Done.",len(model)," words loaded!")
return model
model = loadGloveModel('glove.42B.300d.txt')
# ============================
Output:
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495 words loaded!
# ============================
words = []
for i in preproced_texts:
words.extend(i.split(' '))
for i in preproced_titles:
words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))
inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
words_courpus = {}
words_glove = set(model.keys())
for i in words:
if i in words_glove:
words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
import pickle
with open('glove_vectors', 'wb') as f:
pickle.dump(words_courpus, f)
'''
Out[65]:
In [66]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
model = pickle.load(f)
glove_words = set(model.keys())
#https://stackoverflow.com/questions/59825/how-to-retrieve-an-element-from-a-set-without-removing-it
print(list(glove_words)[0:10])
In [67]:
# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_vectors.append(vector)
print(len(avg_w2v_vectors))
print(len(avg_w2v_vectors[0]))
1.4.2.6 Using Pretrained Models: AVG W2V on `project_title`
In [68]:
# Similarly you can vectorize for title also
# average Word2Vec
# compute average word2vec for each title
avg_w2v_title_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_title): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_title_vectors.append(vector)
print(len(avg_w2v_title_vectors))
print(len(avg_w2v_title_vectors[0]))
1.4.2.7 Using Pretrained Models: TFIDF weighted W2V¶
In [69]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [70]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_vectors.append(vector)
print(len(tfidf_w2v_vectors))
print(len(tfidf_w2v_vectors[0]))
1.4.2.9 Using Pretrained Models: TFIDF weighted W2V on `project_title`
In [71]:
# Similarly you can vectorize for title also
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_title)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [72]:
# average Word2Vec
# compute average word2vec for each project title.
tfidf_w2v_title_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_title): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_title_vectors.append(vector)
print(len(tfidf_w2v_title_vectors))
print(len(tfidf_w2v_title_vectors[0]))
1.4.3 Vectorizing Numerical features¶
In [73]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
price_scalar = StandardScaler()
price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above mean and variance.
price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))
In [74]:
price_standardized
Out[74]:
1.4.3 Vectorizing No. of previously posted projects
In [86]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings("ignore")
prev_proj_scalar = StandardScaler()
prev_proj_scalar.fit(project_data['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {prev_proj_scalar.mean_[0]}, Standard deviation : {np.sqrt(prev_proj_scalar.var_[0])}")
# Now standardize the data with above mean and variance.
prev_proj_standardized = prev_proj_scalar.transform(project_data['price'].values.reshape(-1, 1))
1.4.4 Merging all the above features¶
- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors
In [87]:
print(categories_one_hot.shape)
print(sub_categories_one_hot.shape)
print(text_bow.shape)
print(title_bow.shape)
print(price_standardized.shape)
print(prev_proj_standardized.shape)
In [76]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X = hstack((categories_one_hot, sub_categories_one_hot, text_bow, price_standardized))
X.shape
Out[76]:
Assignment 2: Apply TSNE
If you are using any code snippet from the internet, you have to provide the reference/citations, as we did in the above cells. Otherwise, it will be treated as plagiarism without citations.
- In the above cells we have plotted and analyzed many features. Please observe the plots and write the observations in markdown cells below every plot.
- EDA: Please complete the analysis of the feature: teacher_number_of_previously_posted_projects
-
- Build the data matrix using these features
- school_state : categorical data (one hot encoding)
- clean_categories : categorical data (one hot encoding)
- clean_subcategories : categorical data (one hot encoding)
- teacher_prefix : categorical data (one hot encoding)
- project_grade_category : categorical data (one hot encoding)
- project_title : text data (BOW, TFIDF, AVG W2V, TFIDF W2V)
- price : numerical
- teacher_number_of_previously_posted_projects : numerical
- Now, plot FOUR t-SNE plots with each of these feature sets.
- categorical, numerical features + project_title(BOW)
- categorical, numerical features + project_title(TFIDF)
- categorical, numerical features + project_title(AVG W2V)
- categorical, numerical features + project_title(TFIDF W2V)
- Concatenate all the features and Apply TNSE on the final data matrix
- Note 1: The TSNE accepts only dense matrices
- Note 2: Consider only 5k to 6k data points to avoid memory issues. If you run into memory error issues, reduce the number of data points but clearly state the number of datat-poins you are using
In [77]:
# this is the example code for TSNE
import numpy as np
from sklearn.manifold import TSNE
from sklearn import datasets
import pandas as pd
import matplotlib.pyplot as plt
iris = datasets.load_iris()
x = iris['data']
y = iris['target']
print(type(x),type(y))
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x)
y=y.reshape(-1,1)
print(x.shape,y.shape)
print(type(X_embedding),type(y))
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
for_tsne = np.hstack((X_embedding, y))
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue', 2:'green'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.show()
2.1.1 TSNE with `BOW` encoding of `project_title` feature
In [90]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, title_bow, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on BoW title feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
There is no clear boundary to seperate the approved and rejected points. No inference can be made
2.1.2 TSNE with `TFIDF` encoding of `essay_text` feature
In [91]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, text_bow, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on BoW essay text feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
There is no clear boundary to seperate the approved and rejected points. No inference can be made
2.2.1 TSNE with `TFIDF` encoding of `project_title` feature
In [93]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, title_tfidf , price_standardized, prev_proj_standardized ))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on TFIDF encoding of title feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
Most of the data points are overlapping. No inference can be made
2.2.2 TSNE with `TFIDF` encoding of `text_essay` feature
In [96]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, text_tfidf, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on TFIDF encoding of essay text feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
Most of the data points are overlapping. No inference can be made
2.3.1 TSNE with `AVG W2V` encoding of `project_title` feature
In [95]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, avg_w2v_title_vectors, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on Avg W2V encoding of title feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
As no clustering of same colored data points are observed, it is difficult to come up with a threshold to differentiate both the groups.
So, no inference can be made
2.3.2 TSNE with `AVG W2V` encoding of `text_essay` feature
In [97]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, avg_w2v_vectors, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on Avg W2V encoding of essay text feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
As no clustering of same colored data points are observed, it is difficult to come up with a threshold to differentiate both the groups.
So, no inference can be made
2.4.1 TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature
In [98]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, tfidf_w2v_title_vectors, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on TFIDF Weighted W2V encoding of title feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
Most of the data points are overlapping. No inference can be made
2.4.2 TSNE with `TFIDF Weighted W2V` encoding of `text_essay` feature
In [99]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://www.digitalocean.com/community/tutorials/how-to-plot-data-in-python-3-using-matplotlib
from scipy.sparse import hstack
import matplotlib.patches as mpatches
x = hstack((categories_one_hot, sub_categories_one_hot, grade_cat_one_hot, teacher_prefix_one_hot,
school_state_one_hot, tfidf_w2v_vectors, price_standardized, prev_proj_standardized))
y=project_data['project_is_approved']
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
X_embedding = tsne.fit_transform(x.todense()) #converting x to dense matrix as it is sparse
y=y.as_matrix()
print(x.shape,type(X_embedding))
print(y.shape,type(y))
for_tsne = np.hstack((X_embedding, y.reshape(-1,1))) # -1 means that the dimension is unknown and numpy figures it out
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
#https://stackoverflow.com/questions/39500265/manually-add-legend-items-python-matplotlib
#manually adding legends
red_patch = mpatches.Patch(color='red', label='Rejected')
blue_patch = mpatches.Patch(color='blue', label='Approved')
plt.title('TSNE on TFIDF Weighted W2V encoding of essay text feature')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
Observation¶
Most of the data points are overlapping. No inference can be made
2.5 Summary
- There is no relation between the number of projects submitted by a teacher and their chances of approval
- The percentages of approval and rejection rate did not depend on the presence of numbers in the project summary
- TSNE with BoW, TFIDF, AvgW2V and TFIDF Weighted W2V encodings of project titles didnot produce any clear boundaries to seperate approved and rejected projects
In [ ]: